Overview

This course will give you knowledge of deep learning. After the end of class you will be able to implement many machine learning algorithms like linear regression, clustering and deep neural network to solve many problems. We are going to use TensorFlow to implement deep neural networks.

Availability: In stock

Regular Price: ₹12,750.00

Special Price ₹11,750.00

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Regular Price: ₹12,750.00

Special Price ₹11,750.00

Upcoming Classes

12th Jan 2019
4 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 11750
9th Feb 2019
4 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 11750

Course Description

Prerequisite : Participants must have knowledge of Python .

Course Instructor : Raju Kumar Mishraview more

Objective

Introductory Terms 

  • Data and Data Science.
  • Big Data.
  • Why Big Data.
  • Math and Data Science.
  • Introduction to Statistics.
  • What is learning?
  • Different type of learning.
  • Introduction to Data mining, machine learning.
  • Introduction to artificial intelligence.
  • What is a model?
  • Mathematical models.

NumPy Refresher : 

  • Introduction to NumPy.
  • Ndarray.
  • Array creation
  • Matrix
  • addition, subtraction, multiplication on Array
  • Matrix multiplication. 

MatPlotlib Refresher : 

  • Pyplot as submodule.
  • Scatterplot
  • lineplot
  • histogram
  • PiChart
  • Bar Chart 

TensorFlow Introduction :

  • TensorFlow History.
  • Installing TensorFlow.
  • Introduction to Jupyter.
  • TensorFlow with Jupyter.
  • Introduction to tensor in context of tensor flow.
  • TensorFlow Data types
  • Computation and Dataflow graph
  • Concept of session.
  • Constant
  • Placeholder
  • Variables. 

Mathematical operations in TensorFlow : 

  • Multiplication
  • Summation
  • Maximum
  • Minimum
  • Complex number operations.
  • Some more mathematical functions. 

Matrix operation and Linear algebra in TensorFlow : 

  • Matrix summation and Substraction.
  • Matrix Transpose.
  • Determinant of Matrix.
  • Matrix multiplication.
  • Inverse matrix. 

Linear regression :

  • Introduction to linear regression.
  • Simple linear regression.
  • Parameter estimations.
  • Simple linear regression with TensorFlow.
  • Evaluating our model. 

Logistic Regression :

  • Logistic Regression Introduction.
  • Parameter estimation.
  • With TensorFlow.
  • Model Evaluation. 

Clustering : 

  • Introduction to Clustering
  • Kmeans
  • Kmeans with TensorFlow
  • Optimizing Kmeans
  • Market Segmentation. 

Deep Learning : 

  • Introduction
  • Use cases
  • Why I use deep learning ? 

Introduction to Neural Network : 

  • Biological Neuron an Introduction.
  • Component of biological Neuron.
  • Artificial Neuron.
  • Working of artificial neuron.
  • Activation function
    • Sigmoid function.
    • Linear
    • ReLU
    • Tanh
  • Concept of feed forward.
  • AND, OR and NOT 
  • Perceptron.
  • Perceptron learning algorithm.
  • Implementing Perceptron in TensorFlow. 

Multilayer perceptron : 

  • Concept of gradient descent.
  • Backpropgation algorithm.
  • Problem of vanishing gradient.
  • MLP with TensorFlow.
  • Classifying our data. 

Convolutional  Neural networks (CNN) :

  • Convolutional Neural networks Introduction.
  • Convolutional Layer.
  • Pooling Layer .
  • Connecting fully.
  • Image classification and Convolutional Networks.
  • TensorFlow and CNN
  • Image Classification with TensorFlow.
  • Model evaluation. 

Recurrent Neural network (RNN) : 

  • Introduction
  • Back Propagation through time (BPTT)
  • Need of Memory.
  • Long Short Term memory (LSTM).
  • Bi-Directional RNN
  • Implementing RNN with TensorFlow.
  • Time Series and RNN
  • Sequence prediction with RNN.

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